The Neuroimaging Data WG fulfils the RDA’s mission to build the social and technical bridges that enable open sharing and re-use of data in the domain of neuroimaging. The WG envisions a neuroimaging research landscape in which knowledge is generated in a reproducible fashion (in terms of data, analysis and computation) and coupled with the ability to reuse and extend these studies by others in the community. We aim to shift the way neuroimaging research is performed and reported, with the development and implementation of technology that supports reproducibility at the levels of data management, analysis and utilisation for both within- and between-lab opportunities, including the use of widely distributed and/or large populations to address basic and clinical research questions. In short, our vision is to help neuroimaging researchers: Find and Share data in a FAIR fashion (discover resources); Comprehensively describe their data and analysis workflows (describe research processes); and Manage their computational resource options (do analysis).
The stark realisation that scientific results do not always readily replicate has led some to investigate the root causes of the so-called “reproducibility crisis”. Such self-critical appraisal has been so far more prevalent in Psychology and Neuroscience than in other disciplines, and typically highlight statistical issues, like inadequate statistical designs, as well as poor computational training; problems that are only likely to worsen as data grow larger, become more widely shared, and advanced techniques are imported from fields of engineering, like machine learning.
Specifically, neuroimaging data, in both clinical and fundamental research, have the particularity that they involve a large number of processing steps on a very heterogeneous set of equipment and infrastructures, from the moment they are gathered in proprietary devices (magnetic resonance imaging scanners, electro-encephalography systems, etc) through pre-processing, analysis to annotation, curation and finally deposited into open repositories for others to use in upstream research. A lot of this pipeline remains an error-prone, manual process that relies on the researcher’s voluntary (and unpaid) efforts to acquire an understanding of the infrastructure available and their technical knowledge to use it, to ensure the traceability and provenance of the data, the reproducibility and replicability of the work, and the production of FAIR open datasets, and ultimately train others.
The successful integration of such data into routine neuroimaging practice thus requires neuroscientists to develop skills that fall outside of ordinary training curricula, which should also include data curation, data handling, high performance and on-demand computation (in the “cloud”), semantic web annotation, as well as statistics suitable for large scale inference. The researchers who have been the most receptive to exploring and developing such techniques are typically early career researchers, motivated by the desire to learn, apply and share robust practices. The Neuroimaging Data WG seeks to alleviate some of the biggest challenges they face: They are not formally trained and teach themselves these new data practices following online resources, in isolation and on a voluntary basis. The WG thus fills this gap of support, by pooling interests, experiences and expertises into a platform available globally.
Please see the attached for the full Case Statement including objectives and work plan.